Many software applications require scheduled executions of tasks. For example, a task may be scheduled for a single execution at a specific time, or, in other examples, one or more tasks may be scheduled for recurring execution (e.g., in a periodic or aperiodic fashion) over a period of time. Further, such scheduling (and association executions) of tasks may be implemented in a distributed manner, using a plurality of computing nodes.
However, existing scheduling techniques may require the purchase, installation, and/or implementation of corresponding scheduling components, which may be difficult, time-consuming, and/or expensive to integrate with a particular software application requiring task scheduling. Moreover, such existing scheduling techniques may suffer from an inability to recover efficiently from node failures that may occur during task execution, or, alternatively, may require undesirable or unacceptable quantities of computing resources in order to ensure an acceptable level of recovery. Thus, existing scheduling technologies fail to provide reliable, scalable task scheduling in a manner which leverages existing/available infrastructure to ensure ease of use across a wide variety of applications and platforms.